For anyone who wants to get into Machine learning I have read and can highly recommend these two books:
This one gives a full introduction int ML concepts, including preparing the data, selecting models, optimizions, evaluation, models and ensembling multiple classifiers to boost accuracy. Excellent book for newbies.
The second one assumes you to be somewhat familiar with ML concepts and only focuses on Keras. Keras is a python library that makes writing neural networks a breeze. Some of the best image classification networks in existence can be written in less than 40 lines of code.
I will say though that much of the ML stuff in R is based on data-analytics as opposed to image recognition etc. It’s a really good alternative from the traditional statistical techniques for analysing huge data-sets (i.e. greater than 10k - although I’ve had varying results with around 3k as well).
As for predictive text-books, I’d recommend this one:
Absolutely awesome book that give’s a great explanation for supervised machine learning. Great starting point for anyone interested in ML for data-analytics. It has a great section on data-wrangling and data-manipulation as well (Don’t forget to clean your data, folks!).
This points to what I think is the biggest misconception in ML right now. Using estimators and creating neural networks is the easy part. The real challenge is preparing the data and extracting meaningful features.
To break the WWII Enigma enginge. They trained an AI with german childrens books to get a baseline AI. Then spun up 1000 then later 2000 indepenant AI’s running to break the code and report back. Its an interesting read.
I bit like 1000 Monkey’s er AI’s with typewriters took 1000 years er 13 mins.
Most interesting was the cost to do this. Was under $20 of processing so they say.